論文筆記--Appearance-and-Relation Networks for Video Classification
Appearance-and-Relation Networks for Video Classification是王利民組2017年底的發表的一篇工作。 主要工作是在3D卷積的基礎上，提升了action recognition的準確率，而且沒有像目前主流的two-stream方法那樣使用使用光流信息，因爲光流的提取速度特別慢，這是一個未來的研究趨勢，更不會像IDT那套方法一樣計算複雜。 雖然目前
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相關文章
- 1. 【論文閱讀筆記】Beyond Short Snippets: Deep Networks for Video Classification
- 2. 論文筆記：SlowFast Networks for Video Recognition
- 3. SlowFast Networks for Video Recognition論文筆記
- 4. Appearance-and-Relation Networks for Video Classification
- 5. 【論文閱讀筆記】Large-scale Video Classification with Convolutional Neural Networks
- 6. CV論文筆記（二） Large-scale Video Classification with Convolutional Neural Networks
- 7. [翻譯]Beyond Short Snippets: Deep Networks for Video Classification
- 8. 讀書筆記：Beyond Short Snippets: Deep Networks for Video Classification
- 9. Bag of Tricks for Image Classification with Convolutional Neural Networks論文筆記
- 10. 【論文閱讀筆記】MULTI-SCALE DENSE NETWORKS FOR RESOURCE EFFICIENT IMAGE CLASSIFICATION